Build more accurate AI agents with hybrid reasoning across structured and unstructured data. See how Supervisor Agent improves performance on complex enterprise tasks.

databricks

Databricks demonstrates how their Agent Bricks Supervisor Agent (SA) handles complex enterprise queries requiring reasoning across both structured (databases, knowledge graphs) and unstructured (documents, reviews) data. Built on an internal agentic framework called aroll, SA decomposes multi-constraint questions, routes sub-queries to appropriate tools like Genie spaces and Knowledge Assistants, and synthesizes results across multiple reasoning steps. Benchmarked against state-of-the-art single-turn baselines, SA achieves 20%+ improvements on STaRK (academic, biomedical, product retrieval) and KARLBench (financial analysis, biomedical synthesis, technical troubleshooting). Key findings: multi-step agentic reasoning outperforms single-turn RAG pipelines for complex queries, hybrid structured/unstructured retrieval is critical for tight constraint satisfaction, and a declarative agent-building approach enables high performance without custom code per task.

Agentic Reasoning in Practice: Making Sense of Structured and Unstructured Data

Hybrid Reasoning: Structured Meets Unstructured

The Agentic Advantage on Knowledge-Intensive Tasks